Provides 'R6' objects to perform parallelized hyperparameter
optimization and cross-validation. Hyperparameter optimization can be
performed with Bayesian optimization (via 'ParBayesianOptimization'
<https://cran.r-project.org/package=ParBayesianOptimization>) and grid
search. The optimized hyperparameters can be validated using k-fold
cross-validation. Alternatively, hyperparameter optimization and
validation can be performed with nested cross-validation. While
'mlexperiments' focuses on core wrappers for machine learning
experiments, additional learner algorithms can be supplemented by
inheriting from the provided learner base class.
| Version: |
0.0.4 |
| Depends: |
R (≥ 3.6) |
| Imports: |
data.table, kdry, parallel, progress, R6, splitTools, stats |
| Suggests: |
class, datasets, lintr, mlbench, mlr3measures, ParBayesianOptimization, quarto, rpart, testthat (≥ 3.0.1) |
| Published: |
2024-07-05 |
| DOI: |
10.32614/CRAN.package.mlexperiments |
| Author: |
Lorenz A. Kapsner 
[cre, aut, cph] |
| Maintainer: |
Lorenz A. Kapsner <lorenz.kapsner at gmail.com> |
| BugReports: |
https://github.com/kapsner/mlexperiments/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/kapsner/mlexperiments |
| NeedsCompilation: |
no |
| SystemRequirements: |
Quarto command line tools
(https://github.com/quarto-dev/quarto-cli). |
| CRAN checks: |
mlexperiments results |